Wu Guanlin, Guo Weiming, Zhu Shuai, Fan Gang
School of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
The 2nd Affiliated Hospital of South China University, Hengyang 421001, China.
J Oncol. 2023 Mar 7;2023:5355269. doi: 10.1155/2023/5355269. eCollection 2023.
Traditional studies mostly focus on the role of single gene in regulating clear cell renal cell carcinoma (ccRCC), while it ignores the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database to provide help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. Based on the DEGs of each cluster acquired from single-cell data, immune cell marker genes, and ligand-receptor gene data, we constructed a multilayer network. Then, the genes in the network and the genes in TCGA were used to construct the WGCNA network, which screened out prognosis-associated genes for subsequent analysis. Finally, a prognostic risk scoring model was obtained, and CPTAC data showed that the effectiveness of this model was good. A nomogram based on the predictive model for predicting the overall survival was established, and internal validation was performed well. Our findings suggest that the predictive model built and based on the immune cell scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.
传统研究大多聚焦于单个基因在调控肾透明细胞癌（ccRCC）中的作用，却忽视了肿瘤异质性对疾病进展的影响。本研究的目的是通过分析单细胞数据库中与免疫细胞相关的差异标志物基因，构建ccRCC的预后风险模型，为临床诊断和靶向治疗提供帮助。从相关出版物下载单细胞数据和配体-受体关系对数据，从TCGA和CPTAC下载ccRCC表型和表达谱数据。基于从单细胞数据获得的每个簇的差异表达基因（DEGs）、免疫细胞标志物基因和配体-受体基因数据，我们构建了一个多层网络。然后，将网络中的基因与TCGA中的基因用于构建加权基因共表达网络分析（WGCNA）网络，筛选出与预后相关的基因用于后续分析。最后，获得了一个预后风险评分模型，CPTAC数据表明该模型的有效性良好。建立了基于预测模型的预测总生存的列线图，内部验证效果良好。我们的研究结果表明，基于免疫细胞单细胞RNA测序构建的预测模型将使我们能够判断ccRCC患者的预后，并为基础相关研究和临床实践提供更准确的指导。
